Data Engineer

Role: Sr Data Engineer - Disney

Location: Glendale, CA (Hybrid - 2- 4 Days Onsite)

Full Time

In Person is interview is must

Mandatory skills: Databricks experience (primary requirement), Apache Airflow, Advanced SQL skills, Python, Spark / PySpark, Scala, Experience building and maintaining data pipelines and workflows

Job Description:

Key Responsibilities:

Design, write, test, and deploy data pipelines using PySpark, Scala, SQL, Python

Meet with stakeholders to gather requirements and translate them into scalable data platform solutions

Understanding of Databricks platform and developer tooling to diagnose errors, audit platform activity, and automate updates across pipelines, objects, and integrations

Ability to explain Spark architecture and pipeline behavior to stakeholders to diagnose root causes and recommend solutions

Provide solution architecture across AWS, Databricks, Kubernetes, and Airflow (MWAA), including cross-platform integrations

Manage Databricks platform governance , including Unity Catalog, ACLs, lineage, and data discovery and privacy tooling

Build and maintain Kubernetes containers and containerized utilities supporting deployed data platform services

Apply networking knowledge to troubleshoot connectivity and integration errors across platform components

Perform platform administration: provision and remove access, assess resource utilization, monitor platform health and cost, and evaluate stakeholder requests

Collaborate with engineers, architects, and product managers to drive Core Data platform success; participate in agile/scrum ceremonies

Maintain documentation of platform changes, standards, and pipeline configurations to support data quality and governance

Qualifications:

5+ years of data engineering experience developing and operating large-scale data pipelines

Deep hands-on experience with Databricks and Apache Spark (batch and streaming), including pipeline development in PySpark and/or Scala

Strong understanding of Spark architecture -executors, stages, partitioning, shuffle, and performance tuning-with ability to explain tradeoffs to technical and non-technical stakeholders

Proficiency with Databricks platform tooling (API, SDK, CLI) for automation, auditing, governance, and operational troubleshooting

Proficient in SQL with advanced performance tuning capabilities

Hands-on production experience with Airflow (MWAA) for orchestrating data pipelines

Experience managing Databricks platform governance: ACLs, Unity Catalog, lineage, and access provisioning

Proficiency in Python and at least one additional language (Scala, Kotlin, or SQL-driven pipeline tooling)